from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)

# TODO Update this
_SCREAMING_SNAKE_CASE 		=      {
    """facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
    # See all ESM models at https://huggingface.co/models?filter=esm
}
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				"""esm"""







   def __init__(     self						:						List[Any]   ,   lowerCamelCase_						:						str=None   ,   lowerCamelCase_						:						str=None   ,   lowerCamelCase_						:						int=None   ,   lowerCamelCase_						:						str=768   ,   lowerCamelCase_						:						int=12   ,   lowerCamelCase_						:						Union[str, Any]=12   ,   lowerCamelCase_						:						Optional[Any]=3072   ,   lowerCamelCase_						:						List[Any]=0.1   ,   lowerCamelCase_						:						List[str]=0.1   ,   lowerCamelCase_						:						int=1026   ,   lowerCamelCase_						:						Any=0.0_2   ,   lowerCamelCase_						:						Tuple=1E-12   ,   lowerCamelCase_						:						Any="absolute"   ,   lowerCamelCase_						:						Optional[Any]=True   ,   lowerCamelCase_						:						Union[str, Any]=None   ,   lowerCamelCase_						:						Union[str, Any]=False   ,   lowerCamelCase_						:						Tuple=False   ,   lowerCamelCase_						:						Tuple=None   ,   lowerCamelCase_						:						Union[str, Any]=None   ,   **lowerCamelCase_						:						Optional[Any]   ,   ):





    """simple docstring"""


    super().__init__(pad_token_id=lowerCamelCase_   ,   mask_token_id=lowerCamelCase_   ,   **lowerCamelCase_				)

    UpperCamelCase			  =      vocab_size
    UpperCamelCase			  =      hidden_size
    UpperCamelCase			  =      num_hidden_layers
    UpperCamelCase			  =      num_attention_heads
    UpperCamelCase			  =      intermediate_size
    UpperCamelCase			  =      hidden_dropout_prob
    UpperCamelCase			  =      attention_probs_dropout_prob
    UpperCamelCase			  =      max_position_embeddings
    UpperCamelCase			  =      initializer_range
    UpperCamelCase			  =      layer_norm_eps
    UpperCamelCase			  =      position_embedding_type
    UpperCamelCase			  =      use_cache
    UpperCamelCase			  =      emb_layer_norm_before
    UpperCamelCase			  =      token_dropout
    UpperCamelCase			  =      is_folding_model
    if is_folding_model:
     if esmfold_config is None:
      logger.info("""No esmfold_config supplied for folding model, using default values."""				)
      UpperCamelCase			  =      EsmFoldConfig()
     elif isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
      UpperCamelCase			  =      EsmFoldConfig(**lowerCamelCase_				)
     UpperCamelCase			  =      esmfold_config
     if vocab_list is None:
      logger.warning("""No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"""				)
      UpperCamelCase			  =      get_default_vocab_list()
     else:
      UpperCamelCase			  =      vocab_list
    else:
     UpperCamelCase			  =      None
     UpperCamelCase			  =      None
    if self.esmfold_config is not None and getattr(self.esmfold_config   ,   """use_esm_attn_map"""   ,   lowerCamelCase_				):
     raise ValueError("""The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"""				)







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      super().to_dict()
    if isinstance(self.esmfold_config   ,   lowerCamelCase_				):
     UpperCamelCase			  =      self.esmfold_config.to_dict()
    return output





@dataclass
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				None
   __lowerCAmelCase           =				True
   __lowerCAmelCase           =				False
   __lowerCAmelCase           =				False
   __lowerCAmelCase           =				False
   __lowerCAmelCase           =				0

   __lowerCAmelCase           =				True
   __lowerCAmelCase           =				False

   __lowerCAmelCase           =				128
   __lowerCAmelCase           =				None







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    if self.trunk is None:
     UpperCamelCase			  =      TrunkConfig()
    elif isinstance(self.trunk   ,   lowerCamelCase_				):
     UpperCamelCase			  =      TrunkConfig(**self.trunk				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      asdict(self				)
    UpperCamelCase			  =      self.trunk.to_dict()
    return output





@dataclass
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				48
   __lowerCAmelCase           =				1_024
   __lowerCAmelCase           =				128
   __lowerCAmelCase           =				32
   __lowerCAmelCase           =				32
   __lowerCAmelCase           =				32
   __lowerCAmelCase           =				0
   __lowerCAmelCase           =				0
   __lowerCAmelCase           =				False
   __lowerCAmelCase           =				4
   __lowerCAmelCase           =				128
   __lowerCAmelCase           =				None







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    if self.structure_module is None:
     UpperCamelCase			  =      StructureModuleConfig()
    elif isinstance(self.structure_module   ,   lowerCamelCase_				):
     UpperCamelCase			  =      StructureModuleConfig(**self.structure_module				)

    if self.max_recycles <= 0:
     raise ValueError(f"""`max_recycles` should be positive, got {self.max_recycles}."""				)
    if self.sequence_state_dim % self.sequence_state_dim != 0:
     raise ValueError(
         """`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"""
         f""" {self.sequence_state_dim} and {self.sequence_state_dim}."""				)
    if self.pairwise_state_dim % self.pairwise_state_dim != 0:
     raise ValueError(
         """`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"""
         f""" {self.pairwise_state_dim} and {self.pairwise_state_dim}."""				)

    UpperCamelCase			  =      self.sequence_state_dim // self.sequence_head_width
    UpperCamelCase			  =      self.pairwise_state_dim // self.pairwise_head_width

    if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
     raise ValueError(
         """`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"""
         f""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."""				)
    if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
     raise ValueError(
         """`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"""
         f""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."""				)
    if self.pairwise_state_dim % 2 != 0:
     raise ValueError(f"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."""				)

    if self.dropout >= 0.4:
     raise ValueError(f"""`dropout` should not be greater than 0.4, got {self.dropout}."""				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      asdict(self				)
    UpperCamelCase			  =      self.structure_module.to_dict()
    return output





@dataclass
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				384
   __lowerCAmelCase           =				128
   __lowerCAmelCase           =				16
   __lowerCAmelCase           =				128
   __lowerCAmelCase           =				12
   __lowerCAmelCase           =				4
   __lowerCAmelCase           =				8
   __lowerCAmelCase           =				0.1
   __lowerCAmelCase           =				8
   __lowerCAmelCase           =				1
   __lowerCAmelCase           =				2
   __lowerCAmelCase           =				7
   __lowerCAmelCase           =				10
   __lowerCAmelCase           =				1e-8
   __lowerCAmelCase           =				1e5







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    return asdict(self				)





def 			lowercase(	)		->	List[Any]:







 '''simple docstring'''
 return (
     "<cls>",
     "<pad>",
     "<eos>",
     "<unk>",
     "L",
     "A",
     "G",
     "V",
     "S",
     "E",
     "R",
     "T",
     "I",
     "D",
     "P",
     "K",
     "Q",
     "N",
     "F",
     "Y",
     "M",
     "H",
     "W",
     "C",
     "X",
     "B",
     "U",
     "Z",
     "O",
     ".",
     "-",
     "<null_1>",
     "<mask>",
 )

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				["""image_processor""", """tokenizer"""]
   __lowerCAmelCase           =				"""LayoutLMv3ImageProcessor"""
   __lowerCAmelCase           =				("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						Union[str, Any]=None   ,   lowerCamelCase_						:						Dict=None   ,   **lowerCamelCase_						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      None
    if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead."""   ,   lowerCamelCase_   ,   )
     UpperCamelCase			  =      kwargs.pop("""feature_extractor"""				)

    UpperCamelCase			  =      image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
     raise ValueError("""You need to specify an `image_processor`."""				)
    if tokenizer is None:
     raise ValueError("""You need to specify a `tokenizer`."""				)

    super().__init__(lowerCamelCase_   ,   lowerCamelCase_				)







   def __call__(     self						:						int   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,   lowerCamelCase_						:						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,   lowerCamelCase_						:						Union[List[List[int]], List[List[List[int]]]] = None   ,   lowerCamelCase_						:						Optional[Union[List[int], List[List[int]]]] = None   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Union[bool, str, PaddingStrategy] = False   ,   lowerCamelCase_						:						Union[bool, str, TruncationStrategy] = None   ,   lowerCamelCase_						:						Optional[int] = None   ,   lowerCamelCase_						:						int = 0   ,   lowerCamelCase_						:						Optional[int] = None   ,   lowerCamelCase_						:						Optional[bool] = None   ,   lowerCamelCase_						:						Optional[bool] = None   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = False   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Optional[Union[str, TensorType]] = None   ,   **lowerCamelCase_						:						Optional[int]   ,   ):





    """simple docstring"""


    if self.image_processor.apply_ocr and (boxes is not None):
     raise ValueError(
         """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""				)

    if self.image_processor.apply_ocr and (word_labels is not None):
     raise ValueError(
         """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""				)

    # first, apply the image processor
    UpperCamelCase			  =      self.image_processor(images=lowerCamelCase_   ,   return_tensors=lowerCamelCase_				)

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
     if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
      UpperCamelCase			  =      [text]  # add batch dimension (as the image processor always adds a batch dimension)
     UpperCamelCase			  =      features["""words"""]

    UpperCamelCase			  =      self.tokenizer(
        text=text if text is not None else features["""words"""]   ,   text_pair=text_pair if text_pair is not None else None   ,   boxes=boxes if boxes is not None else features["""boxes"""]   ,   word_labels=lowerCamelCase_   ,   add_special_tokens=lowerCamelCase_   ,   padding=lowerCamelCase_   ,   truncation=lowerCamelCase_   ,   max_length=lowerCamelCase_   ,   stride=lowerCamelCase_   ,   pad_to_multiple_of=lowerCamelCase_   ,   return_token_type_ids=lowerCamelCase_   ,   return_attention_mask=lowerCamelCase_   ,   return_overflowing_tokens=lowerCamelCase_   ,   return_special_tokens_mask=lowerCamelCase_   ,   return_offsets_mapping=lowerCamelCase_   ,   return_length=lowerCamelCase_   ,   verbose=lowerCamelCase_   ,   return_tensors=lowerCamelCase_   ,   **lowerCamelCase_   ,   )

    # add pixel values
    UpperCamelCase			  =      features.pop("""pixel_values"""				)
    if return_overflowing_tokens is True:
     UpperCamelCase			  =      self.get_overflowing_images(lowerCamelCase_   ,   encoded_inputs["""overflow_to_sample_mapping"""]				)
    UpperCamelCase			  =      images

    return encoded_inputs







   def 						lowerCamelCase_	(     self						:						str   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      []
    for sample_idx in overflow_to_sample_mapping:
     images_with_overflow.append(images[sample_idx]				)

    if len(lowerCamelCase_				) != len(lowerCamelCase_				):
     raise ValueError(
         """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
         f""" {len(lowerCamelCase_				)} and {len(lowerCamelCase_				)}"""				)

    return images_with_overflow







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   *lowerCamelCase_						:						Any   ,   **lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    return self.tokenizer.batch_decode(*lowerCamelCase_   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   *lowerCamelCase_						:						Dict   ,   **lowerCamelCase_						:						str				):





    """simple docstring"""


    return self.tokenizer.decode(*lowerCamelCase_   ,   **lowerCamelCase_				)







   @property
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    return ["input_ids", "bbox", "attention_mask", "pixel_values"]







   @property
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,   lowerCamelCase_   ,   )
    return self.image_processor_class







   @property
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    warnings.warn(
        """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,   lowerCamelCase_   ,   )
    return self.image_processor
